Triple
T17383996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doc Emrick |
E422640
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Doc
Doc is the widely used nickname of Mike "Doc" Emrick, the renowned American sportscaster best known for his long career calling National Hockey League games.
|
E422640
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Doc | Statement: [Doc Emrick, nickname, Doc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doc Context triple: [Doc Emrick, nickname, Doc]
-
A.
Doc
Doc is one of the seven dwarfs in Disney's "Snow White and the Seven Dwarfs," characterized as their kindly, bearded leader who often fumbles his words.
-
B.
Doc
Doc is the wise, retired race car and town doctor from the animated film "Cars," who mentors Lightning McQueen.
-
C.
Doc
Doc is a wisecracking, bearded survivor and medic in the post-apocalyptic TV series "Z Nation," known for his laid-back demeanor and unexpected resourcefulness.
-
D.
Doc
Doc is the widely used nickname of Glenn "Doc" Rivers, a former NBA player and championship-winning head coach.
-
E.
Doc
Doc is a gentle, eccentric marine biologist in John Steinbeck’s novel "Cannery Row," known for his intelligence, compassion, and central role in the community’s life.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Doc Triple: [Doc Emrick, nickname, Doc]
Generated description
Doc is the widely used nickname of Mike "Doc" Emrick, the renowned American sportscaster best known for his long career calling National Hockey League games.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Doc Target entity description: Doc is the widely used nickname of Mike "Doc" Emrick, the renowned American sportscaster best known for his long career calling National Hockey League games.
-
A.
Doc
chosen
Doc is the longtime play-by-play announcer Mike "Doc" Emrick, renowned for his iconic voice and decades of work calling National Hockey League games.
-
B.
Doc
Doc is the widely used nickname of Glenn "Doc" Rivers, a former NBA player and championship-winning head coach.
-
C.
Doc
Doc is the nickname of Hall of Fame Major League Baseball pitcher Roy Halladay, renowned for his dominance with the Toronto Blue Jays and Philadelphia Phillies.
-
D.
Doc
Doc is the nickname of Doc Watson, the influential American guitarist and singer known for his flatpicking and folk, bluegrass, and country music.
-
E.
Doc
Doc is the nickname of Dwight Gooden, a dominant Major League Baseball pitcher best known for his stellar early career with the New York Mets in the 1980s.
- F. None of above.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a87d9948190986021f1fb5a4e00 |
completed | April 19, 2026, 2:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a019ff72ca08190a5ea8a1ba6330728 |
completed | May 11, 2026, 9:23 a.m. |
| NEDg | Description generation | batch_6a01a1887d408190baf6d68b17917c09 |
completed | May 11, 2026, 9:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01a2777c188190960046cb7c0b8404 |
completed | May 11, 2026, 9:33 a.m. |
Created at: April 10, 2026, 5:45 a.m.